Identification and extraction of n-grams from text sequence is useful in identifying similar context and to extract different variations through semantics. It can also be used to generate variety of sentences where n-grams are preserved without losing the sequence and meaning. Identifying and extracting n-grams is very essential in many applications of natural language processing, word context disambiguation, and web searching. However, there are high chances of losing contextual information as well as sequence and meaning of the extracted text sequence, if the identification and extraction is not done correctly and in an efficient manner. Conventional systems additionally fail to tackle semantically similar and semantically related words in the n-gram sequence.